Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories

• URL: http://arxiv.org/abs/2502.01503v2
• Date: Thu, 06 Feb 2025 23:01:55 GMT
• Title: Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories
• Authors: Divyagna Bavikadi, Nathaniel Lee, Paulo Shakarian, Chad Parvis,
• Abstract summary: Bad actors in the maritime industry engage in illegal behaviors after disabling their vessel's automatic identification system (AIS)<n>Machine learning approaches only succeed in identifying the locations of these dark vessels'' in the immediate future.<n>We combine concepts from abduction, logic programming, and rule learning to create an efficient method that approaches full recall of dark vessels.
• Score: 0.6037276428689637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bad actors in the maritime industry engage in illegal behaviors after disabling their vessel's automatic identification system (AIS) - which makes finding such vessels difficult for analysts. Machine learning approaches only succeed in identifying the locations of these ``dark vessels'' in the immediate future. This work leverages ideas from the literature on abductive inference applied to locating adversarial agents to solve the problem. Specifically, we combine concepts from abduction, logic programming, and rule learning to create an efficient method that approaches full recall of dark vessels while requiring less search area than machine learning methods. We provide a logic-based paradigm for reasoning about maritime vessels, an abductive inference query method, an automatically extracted rule-based behavior model methodology, and a thorough suite of experiments.

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